tsavage68/chat_1000STEPS_1e5rate_SFT_SFT
The tsavage68/chat_1000STEPS_1e5rate_SFT_SFT is a 7 billion parameter language model, fine-tuned from Meta Llama-2-7b-chat-hf. This model was trained for 1000 steps with a learning rate of 1e-05, achieving a final validation loss of 0.2871. It is a specialized variant of the Llama 2 architecture, optimized through supervised fine-tuning.
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Overview
This model, tsavage68/chat_1000STEPS_1e5rate_SFT_SFT, is a 7 billion parameter language model derived from the Meta Llama-2-7b-chat-hf architecture. It has undergone supervised fine-tuning (SFT) over 1000 training steps.
Training Details
The model was trained using specific hyperparameters, including a learning rate of 1e-05, a train_batch_size of 4, and gradient_accumulation_steps of 2, resulting in a total effective batch size of 8. The optimizer used was Adam with standard betas and epsilon, and a cosine learning rate scheduler with 100 warmup steps. Over 1000 steps, the model achieved a final validation loss of 0.2871.
Key Characteristics
- Base Model: Fine-tuned from
meta-llama/Llama-2-7b-chat-hf. - Parameter Count: 7 billion parameters.
- Training Steps: 1000 steps of supervised fine-tuning.
- Validation Loss: Achieved
0.2871on the evaluation set.
Intended Use
Due to the limited information provided in the original model card regarding its specific training dataset and intended applications, its precise use cases are not explicitly defined. Developers should conduct further evaluation to determine suitability for specific tasks, particularly given its fine-tuned nature from a chat-optimized base model.